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Given that existing retail brand research tends to treat each level of a retail brand as a separate concept, this paper aims to unveil the holistic nature of a multi-level retail brand, considering the distinctiveness of each level and the interrelationships between the images of different levels.

This study uses a scoping review approach that includes 478 retail brand articles. Subsequently, a thematic analysis method is applied.

The brand attributes that shape the distinct image of each retail brand level encompass diverse intrinsic and extrinsic attributes. Moreover, the holistic nature of a multi-level retail brand is formed by the interrelationships between the images of different levels, which are reflected in the presence of common extrinsic attributes and their interplay at attribute, benefit and attitude levels.

Theoretically, this review provides conceptual clarity by unveiling the multi-level yet holistic nature of a retail brand, helping researchers refine and extend existing theories in retail branding, while also providing new research opportunities in this field. Practically, the findings could guide retailers in implementing differentiated branding strategies at each level while achieving synergy across all levels.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

The purpose of this paper is twofold: first, it explores the influencing factors of the government’s trust decision-making in the private sector; second, it explores how these influencing factors affect the government’s trust decisions.

A theoretical model was established, and a questionnaire survey was conducted among 152 professionals. The collected datas were analyzed by the structural equation modeling (SEM) method.

The study identified four critical factors that influence the government’s decision to trust the private sector in public-private-partnership (PPP) projects. All the four factors have a positively correlated impact on the government’s trust decision-making. The structural equation path analysis shows that the most important factor affecting the government’s trust decision-making is the trustee’s (private sector) trustworthy characteristics, and the path coefficient is 0.92. The path coefficients of risk perception and the trustor’s trust tendency are 0.83 and 0.74, respectively. The influence of the legal system environment on government trust decision-making is moderate, with a path coefficient of 0.68.

This paper contributes to the literature in two aspects. First, the factors influencing decision-making to government trust in the private sector in PPP projects have been identified. Second, a comprehensive view of the mechanism of government trust in the private sector in PPP projects has been theorized by the SEM method.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

The Kingdom of Saudi Arabia (KSA) is embracing digital transformation and e-government services, aiming to improve efficiency, accessibility and citizen-centricity. Nonetheless, the country faces challenges such as evolving cyber threats. The purpose of this study is to investigate the factors influencing cybersecurity practices to ensure the reliability and security of e-government services.

This paper investigates the multifaceted dynamics of cybersecurity practices and their impact on the quality and effectiveness of e-government services. Five key factors explored include organizational culture, technology infrastructure, adherence to standards and regulations, employee training and awareness and financial investment in cybersecurity. This study used a quantitative method to gather data from 320 participants. The researcher collected 285 completed questionnaires, excluding unusable or incomplete responses, and analyzed the final data set using partial least squares structural equation modeling.

The findings show that financial investment in cybersecurity, employee training and awareness and adherence to cybersecurity regulations significantly influence the adoption of robust cybersecurity practices. However, the relationship between organizational culture and cybersecurity practices is less straightforward. The research establishes a strong positive correlation between cybersecurity practices and e-government service quality, highlighting the role of security in fostering public trust and user satisfaction and meeting the evolving needs of citizens and businesses.

This research contributes valuable empirical evidence to the fields of e-government and cybersecurity, offering insights that can inform evidence-based policy decisions and resource allocation. By understanding the nuanced dynamics at play, Saudi Arabia is better poised to fortify its digital governance infrastructure and provide secure, high-quality e-government services to its constituents.

The presence of securities crowdfunding (SCF) FinTech in the Islamic financial landscape opens investment opportunities through shares and sukuk (Sharia bond) instruments. This study aims to examine the effect of investment risk (IR), legal risk (LR), product knowledge (PK), Sharia compliance (SC) and subjective norm (SN) on investment decisions in businesses and projects run by small and medium enterprises (SMEs).

The questionnaires were distributed to prospective investors with prior knowledge of SCF and Islamic investment. The data collected was then examined using partial least square-structural equation modeling using SmartPLS 4.0.

The results show that LR has positive and significant implications for supporting investment through SCF, while IR has the opposite. The main findings in this study explain that PK and SC are proven to strengthen the intention to invest in SCF. Meanwhile, SN, which also strengthens intention, is the greatest influence. Therefore, it is highly recommended that SCF organizers collaborate with regulators (OJK), universities, academics and the investor community, as well as Muslim entrepreneurs, to provide education and literacy regarding SCF products and the underlying contracts, along with the consequences and uniqueness of investment vis SCF.

From a managerial side, Sharia expert educators can be appointed to increase investors’ literacy and confidence to support SMEs’ business expansion via SCF. In addition, to minimize investment risk, SCF organizers are also advised to issue sukuk and shares in different low-risk businesses/sectors, followed by investment amounts that are more affordable for novice investors.

Research on SCF as an alternative to SME financing is still scarce. To the best of the author’s knowledge, this is the first research to empirically test the relationship between risk, SC, PK and SN on potential investors’ decisions to support SMEs through the SCF mechanism.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Idea of circular economy defies the classical “make-use-dispose” approach of linear economic model. In the context of health-care industry, it relies heavily on the supply chain practices implemented by industry stakeholders. The purpose of this study is to explore such relationships, study their structure and put it across for attaining sustainability at large.

This study is an empirical research conducted on 145 health-care firms. The collected data is analysed to develop structural and measurement model. The five constructed hypotheses are examined and tested through structural equation modelling.

The study illustrates the latent relationships that exist among the stakeholders involvement, sustainable supply chain practices, sustainable performance and circular economy for health-care industry. It is found that the adoption of sustainable supply chain practices improves health-care performance, which, in turn, have positive influence on circular economy.

The structural and measurement model is developed in the context of circular health-care economy. It can be validated or improvised by conducting similar research in other industry using different methods. This research work fulfils the long existing gap in research by offering a linkage between various constructs to achieve health-care circular economy. Based on the research results, future researchers can build theories of circular economy and sustainability for health-care industry.

The study attempts to study the supply chain ways to achieve circular economy for Indian health-care sector. It considered latent relationships among the set of constructs, which are needed for theory building at later stage.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.